Photometric calibration of the Supernova Legacy Survey fields
Bibliographic record
Abstract
<i>Aims. <i/>We present the photometric calibration of the Supernova Legacy Survey (SNLS) fields. The SNLS aims at measuring the distances to SNe Ia at () using MegaCam, the 1 deg<sup>2<sup/> imager on the Canada-France-Hawaii Telescope (CFHT). The uncertainty affecting the photometric calibration of the survey dominates the systematic uncertainty of the key measurement of the survey, namely the dark energy equation of state. The photometric calibration of the SNLS requires obtaining a uniform response across the imager, calibrating the science field stars in each survey band (SDSS-like <i>ugriz<i/> bands) with respect to standards with known flux in the same bands, and binding the calibration to the <i>UBVRI<i/> Landolt standards used to calibrate the nearby SNe from the literature necessary to produce cosmological constraints.<i>Methods. <i/>The spatial non-uniformities of the imager photometric response are mapped using dithered observations of dense stellar fields. Photometric zero-points against Landolt standards are obtained. The linearity of the instrument is studied.<i>Results. <i/>We show that the imager filters and photometric response are not uniform and publish correction maps. We present models of the effective passbands of the instrument as a function of the position on the focal plane. We define a natural magnitude system for MegaCam. We show that the systematics affecting the magnitude-to-flux relations can be reduced if we use the spectrophotometric standard star BD +17 4708 instead of Vega as a fundamental flux standard. We publish <i>ugriz<i/> catalogs of tertiary standards for all the SNLS fields.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".